1/L The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in Three Missouri Programs Carolyn J. Heinrich LaFollette School of Public Affairs University of Wisconsin-Madison Peter R. Mueser University of Missouri-Columbia Department of Economics Kenneth R. Troske University of Kentucky Department of Economics and Center for Business and Economic Research October 2006
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1/L The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in Three Missouri Programs Carolyn J. Heinrich LaFollette School of Public.
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The Impact of a Temporary Help Job: An Analysis of Outcomes for Participants in
Three Missouri Programs
Carolyn J. HeinrichLaFollette School of Public AffairsUniversity of Wisconsin-Madison
Peter R. MueserUniversity of Missouri-Columbia
Department of Economics
Kenneth R. TroskeUniversity of Kentucky
Department of Economicsand
Center for Business and Economic Research
October 2006
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Introduction
• Employment in temporary help service (THS) firms increased from less than 0.5% in 1982 to over 2.5% by 2004
• Growth was even more dramatic among the most disadvantaged
• Increasingly used as a tool to help those facing difficulties obtaining employment
– Complementary with “work first” programs
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• Temporary help service provider hires worker
• It then contracts with firm for firm to “use” worker
• Worker activity occurs at firm site
• Temporary help service receives payment from firm
• Temp help service provider pays wages, taxes, benefits, etc.
• Firm has no legal employment relationship with worker
• THS is classified as an industry even though work site is in other industry
Introduction (continued) Definition of THS
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• Two views of temp help
– Previously dominant view of temp help
• less job stability
• fewer fringe benefits
• lower wages
– Alternative view
• path to permanent and stable employment
• access to informal training and screening
• consistent with “work-first” strategy
Introduction (continued)
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Introduction (continued)• Our analysis looks at effects of holding a THS job for
those entering three federal programs in Missouri in two different years—1997 and 2001:
– Temporary Assistance for Needy Families (TANF): Single mothers with very low incomes
– Job Training—Job Training Partnership Act (JTPA) in 1997 and Workforce Investment Act (WIA) in 2001: Low income adults & displaced workers
– Employment Exchange (“job service”) Anyone seeking a job
• For each program, individuals are likely to be facing employment difficulties
• But level of job skills differs across program
• As does the severity of the employment shock
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Literature
• Empirical studies confirm that temp help services jobs
– pay lower wages
– offer fewer work hours
– shorter in tenure
– less likely to provide fringe benefits (e.g. health insurance, pensions)
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Literature (continued)
• Causal impact?
– Most studies that look at impact find small or no negative effects of holding a temp help service job on eventual employment success (Lane et al. 2002; Heinrich et al. 2005; Anderson et al. 2002; Segal and Sullivan 1997; Booth et al. 2000)
– One exception is Autor and Houseman (2005) who find that working in temp help does not lead to eventual employment success
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Our contribution
• We examine whether any other industry serves a similar role as THS
• We look at 3 classes of workers who differ by their level of market disadvantage: Do effects differ?
• We look at 7 industry groups: How do other specific industries compare with temporary help?
• We look at how temp help workers succeed: Role of job changes in helping temp help workers succeed?
• We look at whether the effect of temp help varies across the business cycle
• We perform diagnostics to test whether results are likely spurious
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Our findings• Temporary help industry serves a unique role
as a transitional industry• Earnings are lower than in most other
industries• Within 2 years, earnings have largely caught
up• Still, those with initial jobs in some other
industries are doing better (often manufacturing)
• The catch up for temp help workers depends on moving to a better job
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Our findings (continued)
• Results strikingly similar for participants in different programs and for men and women
• Benefits of a job in an alternative industry are slightly larger during a downturn, but basic patterns are similar (2001 vs 1997)
• Effect estimates are not likely to be spurious
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Data
• Participants entering program in calendar year 1997, and 2001
– Focus much of the discussion on 1997 results
• Age at least 18 but less than 65
• Program info from Missouri state administrative sources
• Earnings/employment from the Unemployment Insurance (UI) “wage record data” for both Missouri and Kansas
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Missouri
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• Population: 5.70 m (2003)
• Land area: 178,415 sq km
• Cities:– Kansas City
metro area: 1.12 m
– St. Louis metro area: 2.05 m
– 4 smaller metro areas
Switzerland population: 7.17 m
Portugal population: 10.05 m
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• Population: 5.70 m (2003)
• Land area: 178,415 sq km
• Cities:– Kansas City
metro area: 1.12 m
– St. Louis metro area: 2.05 m
– Columbia metro area 149,000
Missouri is a very “typical” of US states in terms of income, industry, age, race, politics.
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Basic Model
Reference quarter
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Control Variables
• Background: – age, age2
– years of education, high school, college– nonwhite– St. Louis central area– Kansas City central area– suburban, small metro, nonmetro
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• Prior labor market experience– proportion of previous 8 quarters working – working all previous 8 quarters– no work in previous 8 quarters– total earnings in prior year– total earnings two years prior– prior industry
• Quarter of entry (1997:1-1997:4 or 2001:1-2001:4)
• Unemployment in county in outcome quarter
Control Variables (continued)
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Industry code
• One industry in quarter– temporary help services (THS)– manufacturing– retail trade– service (but not THS)– other
• Multiple industries– including THS– not including THS
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Dependent variables
• Basic model– Earnings in outcome quarter (quarter 9)
• OLS• Interpretation is as prediction of “expected
earnings”
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Implicit Assumptions of the Analysis
• We assume that, conditional on the control variables, industry in reference period is not associated with outcome earnings
• Is this reasonable?• We think so:
– Extensive list of control variables including prior work history and prior industry
– Previous paper (Heinrich et al., 2005) controlled for selection and it didn’t matter
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Implicit Assumptions of the Analysis
• Also:– Determinants of industry choice from logit model
reveals very little difference in type of industry– Very similar results with very different samples– Diagnostics suggest that effects estimates are not
likely to be spurious
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Table 1: Distribution of Employment Across Industries Prior and Subsequent to Program Entry in 1997
5. Impact on earnings based on difference-in-difference
1. Initial mean earnings
2. Mean earnings 8 quarters later
3. Mean earnings 8 quarters later controlling characteristics
4. Impact on earnings, relative to no job category
4. Impact on earnings, relative to no job category
Training & Employment Exchange Males
Predicting Quarterly Earnings 1997
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Predicting Employment Probability
• For both men and women employment in any sector in the reference period is strongly positively associated with the probability of employment eight quarters later relative to not having a job.
• Once we control for characteristics there is very little difference between workers in the temp help sector and other sectors in the probability of employment in the future.
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Table 5: Transition between Sectors Over Eight Quarters: Program Entry, 1997